Research methods and key questions

Criminal Justice

Discuss the ethical issues that are of concern to research in criminal justice and how would you resolve them?

In criminal justice research, the primary ethical concerns are avoiding any procedure that may harm the respondents, honoring commitments to respondents and respecting reciprocity, remaining objective and professional in reporting procedures, and protecting the confidentiality and privacy of respondents. To avoid harmful procedures, researchers must analyze possible effects of procedures, and determine any possible long-term or short-term negative effects. If such effects are discovered, full disclosure must be given to the respondents, and those individuals must then give informed consent. To honor commitments and respect trust, researchers must ensure all promises to the respondent are kept, and not use the information given in an improper manner, or treat the respondents improperly. To do so would violate the reciprocal trust vital to the study. To maintain professionalism, the researcher should remain value-free, politically indifferent, disinterested, honest, accurate, and ethical. He or she should avoid misrepresentation of data, generalization, and any technique biased toward a positive yet inaccurate result. Finally, researchers must not disclose personal information about respondents, nor allow identifiable information to be viewed. By using acceptable research methods, unobtrusive data collection, blind and double blind studies, sampling, and informed consent forms, many of these issues can be avoided.

What are the three elements of the classical research design?

The three elements for a classic research design are equivalence, pretests and posttests, and experimental and control groups. Equivalence refers to an effort to assign groups in a way that subjects are comparable, often through randomization and matching. Pretests and posttests are used to ensure effects seen in the study are due to variables within the study. Control groups are necessary to study a population that is not subjected to manipulation.

Why is the classic experiment believed to be the benchmark for research?

The classic experiment is the benchmark for research since all other forms of research design take portions of the classic experiment for the design. In other words, other designs are simply variations of the original model. The basic components of the classic experiment, those of the pre and posttest, control groups, and equivalent groupings make up a part of all other research designs. In addition, since the classic design is the most rigorous, all other designs must take at least some of the validity controls from this design.

Differentiate between internal and external validity.

Internal validity refers to whether the observational process affects the outcome of a study, whereas external validity refers to whether results are unique and applicable only to the group being studied. With internal validity, a researcher is concerned whether a variable other than the treatment variable is responsible for producing a change. In external validity, one is concerned that other variables may affect the generalization possibilities of the findings within a study. Threats to internal validity include history, or events occurring during the course of the study that may affect outcome, maturation, or changes in respondents during the study not caused by the experimental variable, testing, which refers to a bias due to pretesting, instrumentation, or a change in measurement during the study, statistical regression, or the tendency for scores to migrate to the mean, selection bias, experimental mortality, or the loss of subjects, and selection-maturation interactions, or a cause and effect relationship other than the tested variable. Threats to external validity include possible testing effects, or the exposure to pretests, selection bias, reactivity or awareness of being studied, and multiple-treatment interference, or possible effects of multiple treatments on a single group.

What are the three requirements for causality and define each.

The first requirement is that one must demonstrate that a relationship exists between key variables. In other words, one must show that as one variable increases or decreases, another variable follows a predicted pattern in relation to the first. The second requirement is that one must specify the time order of the relationship. In other words, which variable predicts the outcome of another variable? Essentially, this requirement follows login, in that a researcher generally predicts this outcome as part of the planning of the research design. The third requirement is that one must eliminate rival causal factors, and this is the most difficult. A researcher must limit the number of external and internal variables outside of the study variable that could affect the outcome of the study.

What are the disadvantages to the classic experiment?

The primary disadvantage of an experiment is that the controls imposed by a researcher to control for rival causation may create artificial conditions that alter the ability for the results of the study to be generalized to other populations. This is known as artificiality. Other disadvantages include difficulty in mimicking real-life situations in experimental situations, difficulty in retaining subjects, increased ethical considerations, researcher bias, higher risks to internal and external validity, and more difficult generalization of findings.

Differentiate between probability sampling and non-probability sampling.

Probability sampling refers to samples that permit estimation of the likelihood of each element of the population being selected in the sample. These include simple random samples, where each element has an equal likelihood of being placed in each group, stratified random samples, which include placement in a group based on a single demographic characteristic with all other variables as random and equal, cluster sampling, or grouping based on clusters, rather than random assignment, systematic sampling, or the sampling of a specific single occurrence within a group, such as using every eighth soil sample, and multi-stage sampling, or a combination of the above methods. Non-probability samples refer to any sample that violates the concept of equal likelihood of placement. These include quota samples, or stratified samples for a single purpose, accidental samples, such as 'person on the street' sampling, and purposive samples, or samples chosen for a specific variable.

What is the purpose of probability sampling?

Probability sampling provides ways to select samples in which every unit in the test population has an equal chance of appearing in a single sample. This is necessary, since random sampling, and equality of sampling is needed in order to generalize findings to the wider population. By using probability samples, researchers are ensuring a random selection of the population, helping to raise confidence limits, so degree of error is smaller.

Differentiate between the types of probability sampling and the usefulness of each.

Simple random samples, where each element has an equal likelihood of being placed in each group, allow for the use of statistical probabilities, since the sample is equal. Stratified random samples, which include placement in a group based on a single demographic characteristic with all other variables as random and equal, is useful in mimicking particular populations, such as the number of females in a given area. Cluster sampling, or grouping based on clusters, rather than random assignment, is useful in studies where the population is widely dispersed. Systematic sampling, or the sampling of a specific single occurrence within a group, such as using every eighth soil sample, is useful in that each sample is chosen without regard to any other characteristic. Multi-stage sampling, or a combination of the above methods, is useful in that by combining a variety of sampling methods, one can ensure randomization while still controlling for variables.